Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations87396
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.8 MiB
Average record size in memory249.0 B

Variable types

Categorical15
Numeric13
Text3
DateTime1

Alerts

arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
babies is highly imbalanced (96.3%) Imbalance
meal is highly imbalanced (55.0%) Imbalance
distribution_channel is highly imbalanced (59.8%) Imbalance
is_repeated_guest is highly imbalanced (76.2%) Imbalance
reserved_room_type is highly imbalanced (51.3%) Imbalance
deposit_type is highly imbalanced (93.3%) Imbalance
customer_type is highly imbalanced (58.1%) Imbalance
required_car_parking_spaces is highly imbalanced (82.0%) Imbalance
previous_cancellations is highly skewed (γ1 = 34.32374385) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 20.45972514) Skewed
lead_time has 5978 (6.8%) zeros Zeros
stays_in_weekend_nights has 35151 (40.2%) zeros Zeros
stays_in_week_nights has 6175 (7.1%) zeros Zeros
children has 79028 (90.4%) zeros Zeros
previous_cancellations has 85711 (98.1%) zeros Zeros
previous_bookings_not_canceled has 83851 (95.9%) zeros Zeros
booking_changes has 71494 (81.8%) zeros Zeros
days_in_waiting_list has 86536 (99.0%) zeros Zeros
adr has 1778 (2.0%) zeros Zeros
total_of_special_requests has 43894 (50.2%) zeros Zeros

Reproduction

Analysis started2025-04-04 06:46:50.456718
Analysis finished2025-04-04 06:47:20.581835
Duration30.13 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
City Hotel
53428 
Resort Hotel
33968 

Length

Max length12
Median length10
Mean length10.777335
Min length10

Characters and Unicode

Total characters941896
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 53428
61.1%
Resort Hotel 33968
38.9%

Length

2025-04-04T12:17:20.690439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:20.832838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
hotel 87396
50.0%
city 53428
30.6%
resort 33968
 
19.4%

Most occurring characters

ValueCountFrequency (%)
t 174792
18.6%
o 121364
12.9%
e 121364
12.9%
87396
9.3%
H 87396
9.3%
l 87396
9.3%
C 53428
 
5.7%
i 53428
 
5.7%
y 53428
 
5.7%
R 33968
 
3.6%
Other values (2) 67936
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 941896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 174792
18.6%
o 121364
12.9%
e 121364
12.9%
87396
9.3%
H 87396
9.3%
l 87396
9.3%
C 53428
 
5.7%
i 53428
 
5.7%
y 53428
 
5.7%
R 33968
 
3.6%
Other values (2) 67936
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 941896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 174792
18.6%
o 121364
12.9%
e 121364
12.9%
87396
9.3%
H 87396
9.3%
l 87396
9.3%
C 53428
 
5.7%
i 53428
 
5.7%
y 53428
 
5.7%
R 33968
 
3.6%
Other values (2) 67936
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 941896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 174792
18.6%
o 121364
12.9%
e 121364
12.9%
87396
9.3%
H 87396
9.3%
l 87396
9.3%
C 53428
 
5.7%
i 53428
 
5.7%
y 53428
 
5.7%
R 33968
 
3.6%
Other values (2) 67936
 
7.2%

is_canceled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
0
63371 
1
24025 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87396
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63371
72.5%
1 24025
 
27.5%

Length

2025-04-04T12:17:20.937584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:21.034308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 63371
72.5%
1 24025
 
27.5%

Most occurring characters

ValueCountFrequency (%)
0 63371
72.5%
1 24025
 
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 63371
72.5%
1 24025
 
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 63371
72.5%
1 24025
 
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 63371
72.5%
1 24025
 
27.5%

lead_time
Real number (ℝ)

Zeros 

Distinct479
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.891368
Minimum0
Maximum737
Zeros5978
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:21.160753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median49
Q3125
95-th percentile256
Maximum737
Range737
Interquartile range (IQR)114

Descriptive statistics

Standard deviation86.052325
Coefficient of variation (CV)1.0771167
Kurtosis2.1263433
Mean79.891368
Median Absolute Deviation (MAD)44
Skewness1.4317738
Sum6982186
Variance7405.0027
MonotonicityNot monotonic
2025-04-04T12:17:21.299761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5978
 
6.8%
1 3216
 
3.7%
2 1927
 
2.2%
3 1704
 
1.9%
4 1570
 
1.8%
5 1418
 
1.6%
6 1300
 
1.5%
7 1191
 
1.4%
8 1019
 
1.2%
12 915
 
1.0%
Other values (469) 67158
76.8%
ValueCountFrequency (%)
0 5978
6.8%
1 3216
3.7%
2 1927
 
2.2%
3 1704
 
1.9%
4 1570
 
1.8%
5 1418
 
1.6%
6 1300
 
1.5%
7 1191
 
1.4%
8 1019
 
1.2%
9 900
 
1.0%
ValueCountFrequency (%)
737 1
< 0.1%
709 1
< 0.1%
629 2
< 0.1%
626 1
< 0.1%
622 2
< 0.1%
615 2
< 0.1%
608 2
< 0.1%
605 1
< 0.1%
601 2
< 0.1%
594 2
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
2016
42391 
2017
31692 
2015
13313 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters349584
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 42391
48.5%
2017 31692
36.3%
2015 13313
 
15.2%

Length

2025-04-04T12:17:21.426279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:21.526798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2016 42391
48.5%
2017 31692
36.3%
2015 13313
 
15.2%

Most occurring characters

ValueCountFrequency (%)
2 87396
25.0%
0 87396
25.0%
1 87396
25.0%
6 42391
12.1%
7 31692
 
9.1%
5 13313
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 349584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 87396
25.0%
0 87396
25.0%
1 87396
25.0%
6 42391
12.1%
7 31692
 
9.1%
5 13313
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 349584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 87396
25.0%
0 87396
25.0%
1 87396
25.0%
6 42391
12.1%
7 31692
 
9.1%
5 13313
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 349584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 87396
25.0%
0 87396
25.0%
1 87396
25.0%
6 42391
12.1%
7 31692
 
9.1%
5 13313
 
3.8%

arrival_date_month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
August
11257 
July
10057 
May
8355 
April
7908 
June
7765 
Other values (7)
42054 

Length

Max length9
Median length7
Mean length5.8628656
Min length3

Characters and Unicode

Total characters512391
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 11257
12.9%
July 10057
11.5%
May 8355
9.6%
April 7908
9.0%
June 7765
8.9%
March 7513
8.6%
October 6934
7.9%
September 6690
7.7%
February 6098
7.0%
December 5131
5.9%
Other values (2) 9688
11.1%

Length

2025-04-04T12:17:21.650386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 11257
12.9%
july 10057
11.5%
may 8355
9.6%
april 7908
9.0%
june 7765
8.9%
march 7513
8.6%
october 6934
7.9%
september 6690
7.7%
february 6098
7.0%
december 5131
5.9%
Other values (2) 9688
11.1%

Most occurring characters

ValueCountFrequency (%)
e 66250
12.9%
r 56060
 
10.9%
u 51127
 
10.0%
a 31352
 
6.1%
b 29848
 
5.8%
y 29203
 
5.7%
t 24881
 
4.9%
J 22515
 
4.4%
c 19578
 
3.8%
A 19165
 
3.7%
Other values (16) 162412
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 512391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 66250
12.9%
r 56060
 
10.9%
u 51127
 
10.0%
a 31352
 
6.1%
b 29848
 
5.8%
y 29203
 
5.7%
t 24881
 
4.9%
J 22515
 
4.4%
c 19578
 
3.8%
A 19165
 
3.7%
Other values (16) 162412
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 512391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 66250
12.9%
r 56060
 
10.9%
u 51127
 
10.0%
a 31352
 
6.1%
b 29848
 
5.8%
y 29203
 
5.7%
t 24881
 
4.9%
J 22515
 
4.4%
c 19578
 
3.8%
A 19165
 
3.7%
Other values (16) 162412
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 512391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 66250
12.9%
r 56060
 
10.9%
u 51127
 
10.0%
a 31352
 
6.1%
b 29848
 
5.8%
y 29203
 
5.7%
t 24881
 
4.9%
J 22515
 
4.4%
c 19578
 
3.8%
A 19165
 
3.7%
Other values (16) 162412
31.7%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.838334
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:22.037998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median27
Q337
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.674572
Coefficient of variation (CV)0.50951642
Kurtosis-0.95260753
Mean26.838334
Median Absolute Deviation (MAD)11
Skewness0.022503631
Sum2345563
Variance186.99391
MonotonicityNot monotonic
2025-04-04T12:17:22.218491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 2793
 
3.2%
34 2491
 
2.9%
32 2449
 
2.8%
28 2344
 
2.7%
30 2335
 
2.7%
31 2287
 
2.6%
29 2197
 
2.5%
27 2166
 
2.5%
35 2105
 
2.4%
18 2089
 
2.4%
Other values (43) 64140
73.4%
ValueCountFrequency (%)
1 862
1.0%
2 945
1.1%
3 1050
1.2%
4 1125
1.3%
5 1101
1.3%
6 1299
1.5%
7 1630
1.9%
8 1525
1.7%
9 1579
1.8%
10 1630
1.9%
ValueCountFrequency (%)
53 1423
1.6%
52 1061
1.2%
51 786
0.9%
50 1053
1.2%
49 1170
1.3%
48 1199
1.4%
47 1289
1.5%
46 1141
1.3%
45 1315
1.5%
44 1550
1.8%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.815541
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:22.426919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8351464
Coefficient of variation (CV)0.55863701
Kurtosis-1.1962139
Mean15.815541
Median Absolute Deviation (MAD)8
Skewness0.00030825172
Sum1382215
Variance78.059813
MonotonicityNot monotonic
2025-04-04T12:17:22.555670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 3020
 
3.5%
2 3016
 
3.5%
26 3000
 
3.4%
5 2980
 
3.4%
16 2959
 
3.4%
19 2949
 
3.4%
12 2929
 
3.4%
28 2929
 
3.4%
18 2924
 
3.3%
11 2915
 
3.3%
Other values (21) 57775
66.1%
ValueCountFrequency (%)
1 2770
3.2%
2 3016
3.5%
3 2834
3.2%
4 2801
3.2%
5 2980
3.4%
6 2805
3.2%
7 2704
3.1%
8 2809
3.2%
9 2878
3.3%
10 2785
3.2%
ValueCountFrequency (%)
31 1733
2.0%
30 2772
3.2%
29 2880
3.3%
28 2929
3.4%
27 2902
3.3%
26 3000
3.4%
25 2838
3.2%
24 2775
3.2%
23 2776
3.2%
22 2601
3.0%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0052634
Minimum0
Maximum19
Zeros35151
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:22.670413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0319213
Coefficient of variation (CV)1.0265183
Kurtosis7.9934904
Mean1.0052634
Median Absolute Deviation (MAD)1
Skewness1.4116374
Sum87856
Variance1.0648616
MonotonicityNot monotonic
2025-04-04T12:17:22.823430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 35151
40.2%
2 26414
30.2%
1 22657
25.9%
4 1734
 
2.0%
3 1150
 
1.3%
6 113
 
0.1%
5 70
 
0.1%
8 60
 
0.1%
7 15
 
< 0.1%
9 10
 
< 0.1%
Other values (7) 22
 
< 0.1%
ValueCountFrequency (%)
0 35151
40.2%
1 22657
25.9%
2 26414
30.2%
3 1150
 
1.3%
4 1734
 
2.0%
5 70
 
0.1%
6 113
 
0.1%
7 15
 
< 0.1%
8 60
 
0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 5
 
< 0.1%
10 7
 
< 0.1%
9 10
 
< 0.1%
8 60
0.1%
7 15
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6253948
Minimum0
Maximum50
Zeros6175
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:22.968942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0535839
Coefficient of variation (CV)0.78220005
Kurtosis22.103258
Mean2.6253948
Median Absolute Deviation (MAD)1
Skewness2.6913209
Sum229449
Variance4.2172069
MonotonicityNot monotonic
2025-04-04T12:17:23.098457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 22191
25.4%
2 20744
23.7%
3 16259
18.6%
5 9663
11.1%
4 7874
 
9.0%
0 6175
 
7.1%
6 1379
 
1.6%
10 972
 
1.1%
7 967
 
1.1%
8 613
 
0.7%
Other values (25) 559
 
0.6%
ValueCountFrequency (%)
0 6175
 
7.1%
1 22191
25.4%
2 20744
23.7%
3 16259
18.6%
4 7874
 
9.0%
5 9663
11.1%
6 1379
 
1.6%
7 967
 
1.1%
8 613
 
0.7%
9 219
 
0.3%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 5
< 0.1%
26 1
 
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8757952
Minimum0
Maximum55
Zeros385
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:23.201481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62650021
Coefficient of variation (CV)0.33399179
Kurtosis1347.1473
Mean1.8757952
Median Absolute Deviation (MAD)0
Skewness19.859578
Sum163937
Variance0.39250251
MonotonicityNot monotonic
2025-04-04T12:17:23.297492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 64497
73.8%
1 16503
 
18.9%
3 5935
 
6.8%
0 385
 
0.4%
4 60
 
0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 385
 
0.4%
1 16503
 
18.9%
2 64497
73.8%
3 5935
 
6.8%
4 60
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 60
0.1%

children
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13863969
Minimum0
Maximum10
Zeros79028
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:23.397007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45587044
Coefficient of variation (CV)3.2881668
Kurtosis12.957806
Mean0.13863969
Median Absolute Deviation (MAD)0
Skewness3.4636519
Sum12116.555
Variance0.20781786
MonotonicityNot monotonic
2025-04-04T12:17:23.493522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 79028
90.4%
1 4695
 
5.4%
2 3593
 
4.1%
3 75
 
0.1%
0.1386396924 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 79028
90.4%
0.1386396924 4
 
< 0.1%
1 4695
 
5.4%
2 3593
 
4.1%
3 75
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
3 75
 
0.1%
2 3593
 
4.1%
1 4695
 
5.4%
0.1386396924 4
 
< 0.1%
0 79028
90.4%

babies
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
0
86482 
1
 
897
2
 
15
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.0000114
Min length1

Characters and Unicode

Total characters87397
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86482
99.0%
1 897
 
1.0%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Length

2025-04-04T12:17:23.602548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:23.710064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 86482
99.0%
1 897
 
1.0%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 86483
99.0%
1 898
 
1.0%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86483
99.0%
1 898
 
1.0%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86483
99.0%
1 898
 
1.0%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86483
99.0%
1 898
 
1.0%
2 15
 
< 0.1%
9 1
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
BB
67978 
SC
9481 
HB
9085 
Undefined
 
492
FB
 
360

Length

Max length9
Median length2
Mean length2.0394068
Min length2

Characters and Unicode

Total characters178236
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 67978
77.8%
SC 9481
 
10.8%
HB 9085
 
10.4%
Undefined 492
 
0.6%
FB 360
 
0.4%

Length

2025-04-04T12:17:23.846574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:23.959082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
bb 67978
77.8%
sc 9481
 
10.8%
hb 9085
 
10.4%
undefined 492
 
0.6%
fb 360
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B 145401
81.6%
S 9481
 
5.3%
C 9481
 
5.3%
H 9085
 
5.1%
n 984
 
0.6%
d 984
 
0.6%
e 984
 
0.6%
U 492
 
0.3%
f 492
 
0.3%
i 492
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 178236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 145401
81.6%
S 9481
 
5.3%
C 9481
 
5.3%
H 9085
 
5.1%
n 984
 
0.6%
d 984
 
0.6%
e 984
 
0.6%
U 492
 
0.3%
f 492
 
0.3%
i 492
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 178236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 145401
81.6%
S 9481
 
5.3%
C 9481
 
5.3%
H 9085
 
5.1%
n 984
 
0.6%
d 984
 
0.6%
e 984
 
0.6%
U 492
 
0.3%
f 492
 
0.3%
i 492
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 178236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 145401
81.6%
S 9481
 
5.3%
C 9481
 
5.3%
H 9085
 
5.1%
n 984
 
0.6%
d 984
 
0.6%
e 984
 
0.6%
U 492
 
0.3%
f 492
 
0.3%
i 492
 
0.3%
Distinct178
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:24.186267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9926656
Min length2

Characters and Unicode

Total characters261547
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
prt 27453
31.4%
gbr 10433
 
11.9%
fra 8837
 
10.1%
esp 7252
 
8.3%
deu 5387
 
6.2%
ita 3066
 
3.5%
irl 3016
 
3.5%
bel 2081
 
2.4%
bra 1995
 
2.3%
nld 1911
 
2.2%
Other values (168) 15965
18.3%
2025-04-04T12:17:24.563858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 55143
21.1%
P 35858
13.7%
T 32024
12.2%
A 18408
 
7.0%
E 17646
 
6.7%
B 14789
 
5.7%
S 11697
 
4.5%
G 11357
 
4.3%
U 11035
 
4.2%
L 9430
 
3.6%
Other values (16) 44160
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 55143
21.1%
P 35858
13.7%
T 32024
12.2%
A 18408
 
7.0%
E 17646
 
6.7%
B 14789
 
5.7%
S 11697
 
4.5%
G 11357
 
4.3%
U 11035
 
4.2%
L 9430
 
3.6%
Other values (16) 44160
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 55143
21.1%
P 35858
13.7%
T 32024
12.2%
A 18408
 
7.0%
E 17646
 
6.7%
B 14789
 
5.7%
S 11697
 
4.5%
G 11357
 
4.3%
U 11035
 
4.2%
L 9430
 
3.6%
Other values (16) 44160
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 55143
21.1%
P 35858
13.7%
T 32024
12.2%
A 18408
 
7.0%
E 17646
 
6.7%
B 14789
 
5.7%
S 11697
 
4.5%
G 11357
 
4.3%
U 11035
 
4.2%
L 9430
 
3.6%
Other values (16) 44160
16.9%

market_segment
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
Online TA
51618 
Offline TA/TO
13889 
Direct
11804 
Groups
 
4942
Corporate
 
4212
Other values (3)
 
931

Length

Max length13
Median length9
Mean length9.0903817
Min length6

Characters and Unicode

Total characters794463
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 51618
59.1%
Offline TA/TO 13889
 
15.9%
Direct 11804
 
13.5%
Groups 4942
 
5.7%
Corporate 4212
 
4.8%
Complementary 702
 
0.8%
Aviation 227
 
0.3%
Undefined 2
 
< 0.1%

Length

2025-04-04T12:17:24.707347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:24.847858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
online 51618
33.8%
ta 51618
33.8%
offline 13889
 
9.1%
ta/to 13889
 
9.1%
direct 11804
 
7.7%
groups 4942
 
3.2%
corporate 4212
 
2.8%
complementary 702
 
0.5%
aviation 227
 
0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 118058
14.9%
e 82931
10.4%
O 79396
10.0%
T 79396
10.0%
i 77767
9.8%
l 66209
8.3%
A 65734
8.3%
65507
8.2%
f 27780
 
3.5%
r 25872
 
3.3%
Other values (16) 105813
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 794463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 118058
14.9%
e 82931
10.4%
O 79396
10.0%
T 79396
10.0%
i 77767
9.8%
l 66209
8.3%
A 65734
8.3%
65507
8.2%
f 27780
 
3.5%
r 25872
 
3.3%
Other values (16) 105813
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 794463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 118058
14.9%
e 82931
10.4%
O 79396
10.0%
T 79396
10.0%
i 77767
9.8%
l 66209
8.3%
A 65734
8.3%
65507
8.2%
f 27780
 
3.5%
r 25872
 
3.3%
Other values (16) 105813
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 794463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 118058
14.9%
e 82931
10.4%
O 79396
10.0%
T 79396
10.0%
i 77767
9.8%
l 66209
8.3%
A 65734
8.3%
65507
8.2%
f 27780
 
3.5%
r 25872
 
3.3%
Other values (16) 105813
13.3%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
TA/TO
69141 
Direct
12988 
Corporate
 
5081
GDS
 
181
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3772484
Min length3

Characters and Unicode

Total characters469950
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 69141
79.1%
Direct 12988
 
14.9%
Corporate 5081
 
5.8%
GDS 181
 
0.2%
Undefined 5
 
< 0.1%

Length

2025-04-04T12:17:24.994533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:25.151947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 69141
79.1%
direct 12988
 
14.9%
corporate 5081
 
5.8%
gds 181
 
0.2%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 138282
29.4%
/ 69141
14.7%
O 69141
14.7%
A 69141
14.7%
r 23150
 
4.9%
e 18079
 
3.8%
t 18069
 
3.8%
D 13169
 
2.8%
i 12993
 
2.8%
c 12988
 
2.8%
Other values (10) 25797
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 469950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 138282
29.4%
/ 69141
14.7%
O 69141
14.7%
A 69141
14.7%
r 23150
 
4.9%
e 18079
 
3.8%
t 18069
 
3.8%
D 13169
 
2.8%
i 12993
 
2.8%
c 12988
 
2.8%
Other values (10) 25797
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 469950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 138282
29.4%
/ 69141
14.7%
O 69141
14.7%
A 69141
14.7%
r 23150
 
4.9%
e 18079
 
3.8%
t 18069
 
3.8%
D 13169
 
2.8%
i 12993
 
2.8%
c 12988
 
2.8%
Other values (10) 25797
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 469950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 138282
29.4%
/ 69141
14.7%
O 69141
14.7%
A 69141
14.7%
r 23150
 
4.9%
e 18079
 
3.8%
t 18069
 
3.8%
D 13169
 
2.8%
i 12993
 
2.8%
c 12988
 
2.8%
Other values (10) 25797
 
5.5%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
0
83981 
1
 
3415

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87396
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 83981
96.1%
1 3415
 
3.9%

Length

2025-04-04T12:17:25.265697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:25.359444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 83981
96.1%
1 3415
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 83981
96.1%
1 3415
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 83981
96.1%
1 3415
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 83981
96.1%
1 3415
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 83981
96.1%
1 3415
 
3.9%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030413291
Minimum0
Maximum26
Zeros85711
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:25.462194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36914526
Coefficient of variation (CV)12.13763
Kurtosis1726.0943
Mean0.030413291
Median Absolute Deviation (MAD)0
Skewness34.323744
Sum2658
Variance0.13626822
MonotonicityNot monotonic
2025-04-04T12:17:25.583870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 85711
98.1%
1 1407
 
1.6%
2 112
 
0.1%
3 61
 
0.1%
4 30
 
< 0.1%
11 27
 
< 0.1%
5 19
 
< 0.1%
6 17
 
< 0.1%
13 4
 
< 0.1%
25 2
 
< 0.1%
Other values (5) 6
 
< 0.1%
ValueCountFrequency (%)
0 85711
98.1%
1 1407
 
1.6%
2 112
 
0.1%
3 61
 
0.1%
4 30
 
< 0.1%
5 19
 
< 0.1%
6 17
 
< 0.1%
11 27
 
< 0.1%
13 4
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
14 1
 
< 0.1%
13 4
 
< 0.1%
11 27
< 0.1%
6 17
< 0.1%
5 19
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

Skewed  Zeros 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18399011
Minimum0
Maximum72
Zeros83851
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:25.704882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7318937
Coefficient of variation (CV)9.4129715
Kurtosis579.3979
Mean0.18399011
Median Absolute Deviation (MAD)0
Skewness20.459725
Sum16080
Variance2.9994558
MonotonicityNot monotonic
2025-04-04T12:17:25.841392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83851
95.9%
1 1482
 
1.7%
2 580
 
0.7%
3 331
 
0.4%
4 228
 
0.3%
5 181
 
0.2%
6 114
 
0.1%
7 87
 
0.1%
8 70
 
0.1%
9 59
 
0.1%
Other values (63) 413
 
0.5%
ValueCountFrequency (%)
0 83851
95.9%
1 1482
 
1.7%
2 580
 
0.7%
3 331
 
0.4%
4 228
 
0.3%
5 181
 
0.2%
6 114
 
0.1%
7 87
 
0.1%
8 70
 
0.1%
9 59
 
0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
A
56552 
D
17398 
E
6049 
F
 
2823
G
 
2052
Other values (5)
 
2522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87396
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 56552
64.7%
D 17398
 
19.9%
E 6049
 
6.9%
F 2823
 
3.2%
G 2052
 
2.3%
B 999
 
1.1%
C 915
 
1.0%
H 596
 
0.7%
L 6
 
< 0.1%
P 6
 
< 0.1%

Length

2025-04-04T12:17:25.964900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:26.107227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
a 56552
64.7%
d 17398
 
19.9%
e 6049
 
6.9%
f 2823
 
3.2%
g 2052
 
2.3%
b 999
 
1.1%
c 915
 
1.0%
h 596
 
0.7%
l 6
 
< 0.1%
p 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 56552
64.7%
D 17398
 
19.9%
E 6049
 
6.9%
F 2823
 
3.2%
G 2052
 
2.3%
B 999
 
1.1%
C 915
 
1.0%
H 596
 
0.7%
L 6
 
< 0.1%
P 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 56552
64.7%
D 17398
 
19.9%
E 6049
 
6.9%
F 2823
 
3.2%
G 2052
 
2.3%
B 999
 
1.1%
C 915
 
1.0%
H 596
 
0.7%
L 6
 
< 0.1%
P 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 56552
64.7%
D 17398
 
19.9%
E 6049
 
6.9%
F 2823
 
3.2%
G 2052
 
2.3%
B 999
 
1.1%
C 915
 
1.0%
H 596
 
0.7%
L 6
 
< 0.1%
P 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 56552
64.7%
D 17398
 
19.9%
E 6049
 
6.9%
F 2823
 
3.2%
G 2052
 
2.3%
B 999
 
1.1%
C 915
 
1.0%
H 596
 
0.7%
L 6
 
< 0.1%
P 6
 
< 0.1%

assigned_room_type
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
A
46313 
D
22432 
E
7195 
F
 
3627
G
 
2498
Other values (7)
5331 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87396
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 46313
53.0%
D 22432
25.7%
E 7195
 
8.2%
F 3627
 
4.2%
G 2498
 
2.9%
C 2165
 
2.5%
B 1820
 
2.1%
H 706
 
0.8%
I 357
 
0.4%
K 276
 
0.3%
Other values (2) 7
 
< 0.1%

Length

2025-04-04T12:17:26.238969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 46313
53.0%
d 22432
25.7%
e 7195
 
8.2%
f 3627
 
4.2%
g 2498
 
2.9%
c 2165
 
2.5%
b 1820
 
2.1%
h 706
 
0.8%
i 357
 
0.4%
k 276
 
0.3%
Other values (2) 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 46313
53.0%
D 22432
25.7%
E 7195
 
8.2%
F 3627
 
4.2%
G 2498
 
2.9%
C 2165
 
2.5%
B 1820
 
2.1%
H 706
 
0.8%
I 357
 
0.4%
K 276
 
0.3%
Other values (2) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 46313
53.0%
D 22432
25.7%
E 7195
 
8.2%
F 3627
 
4.2%
G 2498
 
2.9%
C 2165
 
2.5%
B 1820
 
2.1%
H 706
 
0.8%
I 357
 
0.4%
K 276
 
0.3%
Other values (2) 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 46313
53.0%
D 22432
25.7%
E 7195
 
8.2%
F 3627
 
4.2%
G 2498
 
2.9%
C 2165
 
2.5%
B 1820
 
2.1%
H 706
 
0.8%
I 357
 
0.4%
K 276
 
0.3%
Other values (2) 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 46313
53.0%
D 22432
25.7%
E 7195
 
8.2%
F 3627
 
4.2%
G 2498
 
2.9%
C 2165
 
2.5%
B 1820
 
2.1%
H 706
 
0.8%
I 357
 
0.4%
K 276
 
0.3%
Other values (2) 7
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27160282
Minimum0
Maximum21
Zeros71494
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:26.340728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72724527
Coefficient of variation (CV)2.6776057
Kurtosis67.615632
Mean0.27160282
Median Absolute Deviation (MAD)0
Skewness5.5463283
Sum23737
Variance0.52888568
MonotonicityNot monotonic
2025-04-04T12:17:26.474466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 71494
81.8%
1 10902
 
12.5%
2 3508
 
4.0%
3 875
 
1.0%
4 356
 
0.4%
5 116
 
0.1%
6 59
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
Other values (11) 30
 
< 0.1%
ValueCountFrequency (%)
0 71494
81.8%
1 10902
 
12.5%
2 3508
 
4.0%
3 875
 
1.0%
4 356
 
0.4%
5 116
 
0.1%
6 59
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 3
< 0.1%
14 5
< 0.1%
13 5
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
No Deposit
86251 
Non Refund
 
1038
Refundable
 
107

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters873960
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 86251
98.7%
Non Refund 1038
 
1.2%
Refundable 107
 
0.1%

Length

2025-04-04T12:17:26.586202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:26.684936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
no 86251
49.4%
deposit 86251
49.4%
non 1038
 
0.6%
refund 1038
 
0.6%
refundable 107
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 173540
19.9%
e 87503
10.0%
N 87289
10.0%
87289
10.0%
s 86251
9.9%
i 86251
9.9%
t 86251
9.9%
p 86251
9.9%
D 86251
9.9%
n 2183
 
0.2%
Other values (7) 4901
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 873960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 173540
19.9%
e 87503
10.0%
N 87289
10.0%
87289
10.0%
s 86251
9.9%
i 86251
9.9%
t 86251
9.9%
p 86251
9.9%
D 86251
9.9%
n 2183
 
0.2%
Other values (7) 4901
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 873960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 173540
19.9%
e 87503
10.0%
N 87289
10.0%
87289
10.0%
s 86251
9.9%
i 86251
9.9%
t 86251
9.9%
p 86251
9.9%
D 86251
9.9%
n 2183
 
0.2%
Other values (7) 4901
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 873960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 173540
19.9%
e 87503
10.0%
N 87289
10.0%
87289
10.0%
s 86251
9.9%
i 86251
9.9%
t 86251
9.9%
p 86251
9.9%
D 86251
9.9%
n 2183
 
0.2%
Other values (7) 4901
 
0.6%

agent
Text

Distinct334
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:26.969182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.1623873
Min length1

Characters and Unicode

Total characters188984
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)0.1%

Sample

1st rowNULL
2nd rowNULL
3rd rowNULL
4th row304
5th row240
ValueCountFrequency (%)
9 28759
32.9%
240 13028
14.9%
null 12193
14.0%
14 3349
 
3.8%
7 3300
 
3.8%
250 2779
 
3.2%
241 1644
 
1.9%
28 1502
 
1.7%
8 1383
 
1.6%
1 1232
 
1.4%
Other values (324) 18227
20.9%
2025-04-04T12:17:27.438176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 31325
16.6%
2 25444
13.5%
L 24386
12.9%
4 23445
12.4%
0 17970
9.5%
1 14829
7.8%
N 12193
 
6.5%
U 12193
 
6.5%
5 6328
 
3.3%
7 6165
 
3.3%
Other values (3) 14706
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 31325
16.6%
2 25444
13.5%
L 24386
12.9%
4 23445
12.4%
0 17970
9.5%
1 14829
7.8%
N 12193
 
6.5%
U 12193
 
6.5%
5 6328
 
3.3%
7 6165
 
3.3%
Other values (3) 14706
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 31325
16.6%
2 25444
13.5%
L 24386
12.9%
4 23445
12.4%
0 17970
9.5%
1 14829
7.8%
N 12193
 
6.5%
U 12193
 
6.5%
5 6328
 
3.3%
7 6165
 
3.3%
Other values (3) 14706
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 31325
16.6%
2 25444
13.5%
L 24386
12.9%
4 23445
12.4%
0 17970
9.5%
1 14829
7.8%
N 12193
 
6.5%
U 12193
 
6.5%
5 6328
 
3.3%
7 6165
 
3.3%
Other values (3) 14706
7.8%
Distinct353
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:27.736421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9178681
Min length1

Characters and Unicode

Total characters342406
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)0.1%

Sample

1st rowNULL
2nd rowNULL
3rd rowNULL
4th rowNULL
5th rowNULL
ValueCountFrequency (%)
null 82137
94.0%
40 851
 
1.0%
223 503
 
0.6%
45 238
 
0.3%
153 206
 
0.2%
154 133
 
0.2%
219 131
 
0.1%
174 121
 
0.1%
281 119
 
0.1%
233 95
 
0.1%
Other values (343) 2862
 
3.3%
2025-04-04T12:17:28.163167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 164274
48.0%
N 82137
24.0%
U 82137
24.0%
2 2380
 
0.7%
4 2253
 
0.7%
3 1915
 
0.6%
1 1902
 
0.6%
0 1468
 
0.4%
5 1185
 
0.3%
8 785
 
0.2%
Other values (3) 1970
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 342406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 164274
48.0%
N 82137
24.0%
U 82137
24.0%
2 2380
 
0.7%
4 2253
 
0.7%
3 1915
 
0.6%
1 1902
 
0.6%
0 1468
 
0.4%
5 1185
 
0.3%
8 785
 
0.2%
Other values (3) 1970
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 342406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 164274
48.0%
N 82137
24.0%
U 82137
24.0%
2 2380
 
0.7%
4 2253
 
0.7%
3 1915
 
0.6%
1 1902
 
0.6%
0 1468
 
0.4%
5 1185
 
0.3%
8 785
 
0.2%
Other values (3) 1970
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 342406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 164274
48.0%
N 82137
24.0%
U 82137
24.0%
2 2380
 
0.7%
4 2253
 
0.7%
3 1915
 
0.6%
1 1902
 
0.6%
0 1468
 
0.4%
5 1185
 
0.3%
8 785
 
0.2%
Other values (3) 1970
 
0.6%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7495652
Minimum0
Maximum391
Zeros86536
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:28.309624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.015731
Coefficient of variation (CV)13.362054
Kurtosis480.60911
Mean0.7495652
Median Absolute Deviation (MAD)0
Skewness19.396419
Sum65509
Variance100.31487
MonotonicityNot monotonic
2025-04-04T12:17:28.469435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86536
99.0%
63 53
 
0.1%
87 25
 
< 0.1%
44 23
 
< 0.1%
15 22
 
< 0.1%
58 21
 
< 0.1%
48 21
 
< 0.1%
122 21
 
< 0.1%
38 19
 
< 0.1%
77 17
 
< 0.1%
Other values (118) 638
 
0.7%
ValueCountFrequency (%)
0 86536
99.0%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 2
 
< 0.1%
4 15
 
< 0.1%
5 8
 
< 0.1%
6 9
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
391 5
< 0.1%
379 3
 
< 0.1%
330 4
 
< 0.1%
259 9
< 0.1%
236 7
< 0.1%
224 5
< 0.1%
223 10
< 0.1%
215 4
 
< 0.1%
207 5
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
Transient
71986 
Transient-Party
11727 
Contract
 
3139
Group
 
544

Length

Max length15
Median length9
Mean length9.7442789
Min length5

Characters and Unicode

Total characters851611
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 71986
82.4%
Transient-Party 11727
 
13.4%
Contract 3139
 
3.6%
Group 544
 
0.6%

Length

2025-04-04T12:17:28.618453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:28.723961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
transient 71986
82.4%
transient-party 11727
 
13.4%
contract 3139
 
3.6%
group 544
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 170565
20.0%
t 101718
11.9%
r 99123
11.6%
a 98579
11.6%
T 83713
9.8%
s 83713
9.8%
i 83713
9.8%
e 83713
9.8%
y 11727
 
1.4%
- 11727
 
1.4%
Other values (7) 23320
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 851611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 170565
20.0%
t 101718
11.9%
r 99123
11.6%
a 98579
11.6%
T 83713
9.8%
s 83713
9.8%
i 83713
9.8%
e 83713
9.8%
y 11727
 
1.4%
- 11727
 
1.4%
Other values (7) 23320
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 851611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 170565
20.0%
t 101718
11.9%
r 99123
11.6%
a 98579
11.6%
T 83713
9.8%
s 83713
9.8%
i 83713
9.8%
e 83713
9.8%
y 11727
 
1.4%
- 11727
 
1.4%
Other values (7) 23320
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 851611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 170565
20.0%
t 101718
11.9%
r 99123
11.6%
a 98579
11.6%
T 83713
9.8%
s 83713
9.8%
i 83713
9.8%
e 83713
9.8%
y 11727
 
1.4%
- 11727
 
1.4%
Other values (7) 23320
 
2.7%

adr
Real number (ℝ)

Zeros 

Distinct8879
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.33725
Minimum-6.38
Maximum5400
Zeros1778
Zeros (%)2.0%
Negative1
Negative (%)< 0.1%
Memory size682.9 KiB
2025-04-04T12:17:28.865776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile36.83
Q172
median98.1
Q3134
95-th percentile204
Maximum5400
Range5406.38
Interquartile range (IQR)62

Descriptive statistics

Standard deviation55.013953
Coefficient of variation (CV)0.51735356
Kurtosis981.62077
Mean106.33725
Median Absolute Deviation (MAD)30.45
Skewness10.921447
Sum9293450
Variance3026.5351
MonotonicityNot monotonic
2025-04-04T12:17:29.003608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1778
 
2.0%
75 1320
 
1.5%
65 1260
 
1.4%
48 878
 
1.0%
85 858
 
1.0%
95 850
 
1.0%
90 818
 
0.9%
80 754
 
0.9%
99 749
 
0.9%
60 730
 
0.8%
Other values (8869) 77401
88.6%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 1778
2.0%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 13
 
< 0.1%
1.29 1
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
510 1
< 0.1%
508 1
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
0
80083 
1
 
7280
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87396
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80083
91.6%
1 7280
 
8.3%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2025-04-04T12:17:29.150782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:29.254289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 80083
91.6%
1 7280
 
8.3%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 80083
91.6%
1 7280
 
8.3%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80083
91.6%
1 7280
 
8.3%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80083
91.6%
1 7280
 
8.3%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80083
91.6%
1 7280
 
8.3%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69856744
Minimum0
Maximum5
Zeros43894
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size682.9 KiB
2025-04-04T12:17:29.355797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83194598
Coefficient of variation (CV)1.1909315
Kurtosis0.81986954
Mean0.69856744
Median Absolute Deviation (MAD)0
Skewness1.0828516
Sum61052
Variance0.69213411
MonotonicityNot monotonic
2025-04-04T12:17:29.452305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 43894
50.2%
1 29017
33.2%
2 11812
 
13.5%
3 2317
 
2.7%
4 320
 
0.4%
5 36
 
< 0.1%
ValueCountFrequency (%)
0 43894
50.2%
1 29017
33.2%
2 11812
 
13.5%
3 2317
 
2.7%
4 320
 
0.4%
5 36
 
< 0.1%
ValueCountFrequency (%)
5 36
 
< 0.1%
4 320
 
0.4%
3 2317
 
2.7%
2 11812
 
13.5%
1 29017
33.2%
0 43894
50.2%

reservation_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
Check-Out
63371 
Canceled
23011 
No-Show
 
1014

Length

Max length9
Median length9
Mean length8.7134995
Min length7

Characters and Unicode

Total characters761525
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 63371
72.5%
Canceled 23011
 
26.3%
No-Show 1014
 
1.2%

Length

2025-04-04T12:17:29.574340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T12:17:29.688848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
check-out 63371
72.5%
canceled 23011
 
26.3%
no-show 1014
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 109393
14.4%
C 86382
11.3%
c 86382
11.3%
h 64385
8.5%
- 64385
8.5%
u 63371
8.3%
t 63371
8.3%
O 63371
8.3%
k 63371
8.3%
a 23011
 
3.0%
Other values (7) 74103
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 761525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 109393
14.4%
C 86382
11.3%
c 86382
11.3%
h 64385
8.5%
- 64385
8.5%
u 63371
8.3%
t 63371
8.3%
O 63371
8.3%
k 63371
8.3%
a 23011
 
3.0%
Other values (7) 74103
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 761525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 109393
14.4%
C 86382
11.3%
c 86382
11.3%
h 64385
8.5%
- 64385
8.5%
u 63371
8.3%
t 63371
8.3%
O 63371
8.3%
k 63371
8.3%
a 23011
 
3.0%
Other values (7) 74103
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 761525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 109393
14.4%
C 86382
11.3%
c 86382
11.3%
h 64385
8.5%
- 64385
8.5%
u 63371
8.3%
t 63371
8.3%
O 63371
8.3%
k 63371
8.3%
a 23011
 
3.0%
Other values (7) 74103
9.7%
Distinct926
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size682.9 KiB
Minimum2014-10-17 00:00:00
Maximum2017-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-04T12:17:29.835174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:29.977923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-04-04T12:17:17.748551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.073207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.768199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.324938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.781631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.435481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.043150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.545595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.168394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.638192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.153594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.759664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.343962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.882078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.223225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.877786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.437462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.909950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.568223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.161731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.654107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.318939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.750716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.273102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.879800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.464641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.994741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.373153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.983816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.551962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.033390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.671890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.269397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.751618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.430516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.868378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.390617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.993315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.577231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.111924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.523227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.093321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.658475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.146296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.788222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.406427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.865211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.557969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.981970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.492128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.138422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.692941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.223701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.691276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.209908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.773991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.272973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.918810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.532944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.971895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.670492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.107087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.609159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.300611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.820003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.384501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.803099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.334346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.885066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.387007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.069256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.653465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.126596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.779215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.215613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.707730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.410839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.919737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.496047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:16:59.907787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.431859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.005562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.491525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.173780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.754974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.232108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.880804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.327195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.805248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.513348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.028241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.593555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.025305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.532373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.114074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.601065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.307995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:07.875505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.333042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.980321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.440893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.914068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.620861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.124757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.697649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.183716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.639184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.228812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.718574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.420533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.000562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.577067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.093348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.548475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.032507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.730377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.222272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.810190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.314148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.883191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.345404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.851311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.535058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.111076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.696788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.202889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.686302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.147026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.860944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.330798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:18.908691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.419742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:01.985021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.453084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:04.968913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.639869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.214588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.833687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.310476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.802642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.241537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:15.970543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.427537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:19.020200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.556177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.111790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.573600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.219460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.771578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.323099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:09.947207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.432163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:12.916323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.370262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.107719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.539280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:19.119880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:00.659690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:02.213425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:03.676119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:05.324968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:06.917617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:08.436920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:10.067878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:11.539679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:13.029926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:14.464777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:16.213228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-04T12:17:17.633970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-04T12:17:30.479632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
adradultsarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requests
adr1.0000.3400.0190.0000.1140.0000.0000.000-0.0110.2840.000-0.0450.0300.0000.0000.0000.0000.1080.0000.000-0.175-0.0810.0000.0030.0000.0850.0480.162
adults0.3401.0000.0030.0130.0260.0200.0000.000-0.0740.0700.092-0.0330.0000.0080.0140.0190.0000.2330.0080.000-0.245-0.0870.0000.0130.0000.1730.1320.159
arrival_date_day_of_month0.0190.0031.0000.0490.0880.0250.0090.0050.0050.0170.0230.0120.0390.0230.0230.0120.0140.0120.0190.021-0.002-0.0040.0060.0100.008-0.017-0.009-0.003
arrival_date_month0.0000.0130.0491.0000.8000.4100.0280.0170.0100.0490.1000.0350.0510.0700.0590.0850.0940.1220.0770.0680.0190.0210.0140.0690.0470.0390.0490.043
arrival_date_week_number0.1140.0260.0880.8001.0000.4040.0280.0150.0180.0170.099-0.0000.0470.0630.0590.0760.0950.0970.0690.062-0.0470.0480.0110.0620.0440.0410.0370.044
arrival_date_year0.0000.0200.0250.4100.4041.0000.0530.0180.0110.0190.1850.0340.0220.0430.1040.0880.0260.1030.1190.0960.0260.0240.0300.0660.0620.0230.0210.057
assigned_room_type0.0000.0000.0090.0280.0280.0531.0000.0420.0500.3250.0830.0140.0610.0840.3710.0940.0680.0280.0800.1510.0020.0000.0790.0670.7780.0430.0450.033
babies0.0000.0000.0050.0170.0150.0180.0421.0000.0150.0000.0070.0000.0050.0280.0440.0200.0120.0000.0310.0260.0000.0000.0160.0140.0360.0000.0060.059
booking_changes-0.011-0.0740.0050.0100.0180.0110.0500.0151.0000.0480.0520.0440.0000.0260.0300.0320.0000.0560.0310.0200.018-0.0160.0110.0220.0100.0520.022-0.005
children0.2840.0700.0170.0490.0170.0190.3250.0000.0481.0000.031-0.0290.0150.0330.0260.0670.0330.0510.0680.039-0.052-0.0300.0130.0530.3850.0450.0360.056
customer_type0.0000.0920.0230.1000.0990.1850.0830.0070.0520.0311.0000.0920.0500.0890.0610.1270.1080.1110.3270.0980.0100.0000.0260.0900.0920.0870.1000.076
days_in_waiting_list-0.045-0.0330.0120.035-0.0000.0340.0140.0000.044-0.0290.0921.0000.1150.0130.0480.0140.0110.0980.0730.050-0.0060.0200.0470.0100.0150.006-0.038-0.070
deposit_type0.0300.0000.0390.0510.0470.0220.0610.0050.0000.0150.0500.1151.0000.0360.0560.1650.0150.1740.2140.0320.0000.0490.0340.1190.0480.0120.0190.077
distribution_channel0.0000.0080.0230.0700.0630.0430.0840.0280.0260.0330.0890.0130.0361.0000.1410.1520.3540.1030.7160.0870.1250.0530.0720.1130.1000.0130.0690.097
hotel0.0000.0140.0230.0590.0590.1040.3710.0440.0300.0260.0610.0480.0560.1411.0000.0710.0460.0900.1800.3480.0230.0250.2180.0730.3110.1820.1840.022
is_canceled0.0000.0190.0120.0850.0760.0880.0940.0200.0320.0670.1270.0140.1650.1520.0711.0000.0900.1680.2210.0630.0370.0150.1861.0000.0570.0450.0590.131
is_repeated_guest0.0000.0000.0140.0940.0950.0260.0680.0120.0000.0330.1080.0110.0150.3540.0460.0901.0000.1380.3960.0700.3370.1960.0730.0910.0590.0240.1030.046
lead_time0.1080.2330.0120.1220.0970.1030.0280.0000.0560.0510.1110.0980.1740.1030.0900.1680.1381.0000.1290.070-0.198-0.0000.0410.1260.0360.4180.2950.063
market_segment0.0000.0080.0190.0770.0690.1190.0800.0310.0310.0680.3270.0730.2140.7160.1800.2210.3960.1291.0000.1640.1060.0560.0750.1620.1020.0450.0790.163
meal0.0000.0000.0210.0680.0620.0960.1510.0260.0200.0390.0980.0500.0320.0870.3480.0630.0700.0700.1641.0000.0150.0360.0390.0460.1350.0580.0810.039
previous_bookings_not_canceled-0.175-0.245-0.0020.019-0.0470.0260.0020.0000.018-0.0520.010-0.0060.0000.1250.0230.0370.337-0.1980.1060.0151.0000.2430.0160.0260.008-0.139-0.113-0.004
previous_cancellations-0.081-0.087-0.0040.0210.0480.0240.0000.000-0.016-0.0300.0000.0200.0490.0530.0250.0150.196-0.0000.0560.0360.2431.0000.0000.0090.000-0.031-0.028-0.009
required_car_parking_spaces0.0000.0000.0060.0140.0110.0300.0790.0160.0110.0130.0260.0470.0340.0720.2180.1860.0730.0410.0750.0390.0160.0001.0000.1310.0680.0190.0230.025
reservation_status0.0030.0130.0100.0690.0620.0660.0670.0140.0220.0530.0900.0100.1190.1130.0731.0000.0910.1260.1620.0460.0260.0090.1311.0000.0430.0390.0460.093
reserved_room_type0.0000.0000.0080.0470.0440.0620.7780.0360.0100.3850.0920.0150.0480.1000.3110.0570.0590.0360.1020.1350.0080.0000.0680.0431.0000.0400.0470.045
stays_in_week_nights0.0850.173-0.0170.0390.0410.0230.0430.0000.0520.0450.0870.0060.0120.0130.1820.0450.0240.4180.0450.058-0.139-0.0310.0190.0390.0401.0000.3270.056
stays_in_weekend_nights0.0480.132-0.0090.0490.0370.0210.0450.0060.0220.0360.100-0.0380.0190.0690.1840.0590.1030.2950.0790.081-0.113-0.0280.0230.0460.0470.3271.0000.036
total_of_special_requests0.1620.159-0.0030.0430.0440.0570.0330.059-0.0050.0560.076-0.0700.0770.0970.0220.1310.0460.0630.1630.039-0.004-0.0090.0250.0930.0450.0560.0361.000

Missing values

2025-04-04T12:17:19.375055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-04T12:17:19.985068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No DepositNULLNULL0Transient0.000Check-Out2015-07-01
1Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No DepositNULLNULL0Transient0.000Check-Out2015-07-01
2Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No DepositNULLNULL0Transient75.000Check-Out2015-07-02
3Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No Deposit304NULL0Transient75.000Check-Out2015-07-02
4Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240NULL0Transient98.001Check-Out2015-07-03
5Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No DepositNULLNULL0Transient107.000Check-Out2015-07-03
6Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No Deposit303NULL0Transient103.001Check-Out2015-07-03
7Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240NULL0Transient82.001Canceled2015-05-06
8Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15NULL0Transient105.500Canceled2015-04-22
9Resort Hotel1232015July2710420.00BBPRTOnline TATA/TO000EE0No Deposit240NULL0Transient123.000Canceled2015-06-23
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
87386City Hotel0442017August35311320.00SCDEUOnline TATA/TO000AA0No Deposit9NULL0Transient140.7501Check-Out2017-09-04
87387City Hotel01882017August35312320.00BBDEUDirectDirect000AA0No Deposit14NULL0Transient99.0000Check-Out2017-09-05
87388City Hotel01352017August35302430.00BBJPNOnline TATA/TO000GG0No Deposit7NULL0Transient209.0000Check-Out2017-09-05
87389City Hotel01642017August35312420.00BBDEUOffline TA/TOTA/TO000AA0No Deposit42NULL0Transient87.6000Check-Out2017-09-06
87390City Hotel0212017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394NULL0Transient96.1402Check-Out2017-09-06
87391City Hotel0232017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394NULL0Transient96.1400Check-Out2017-09-06
87392City Hotel01022017August35312530.00BBFRAOnline TATA/TO000EE0No Deposit9NULL0Transient225.4302Check-Out2017-09-07
87393City Hotel0342017August35312520.00BBDEUOnline TATA/TO000DD0No Deposit9NULL0Transient157.7104Check-Out2017-09-07
87394City Hotel01092017August35312520.00BBGBROnline TATA/TO000AA0No Deposit89NULL0Transient104.4000Check-Out2017-09-07
87395City Hotel02052017August35292720.00HBDEUOnline TATA/TO000AA0No Deposit9NULL0Transient151.2002Check-Out2017-09-07